1

Innovation spillovers and firm performance:

Micro evidence from Spain (2004-2009)

Esther Goyaa, Esther Vayá, Jordi Suriñach

AQR-IREA Research Group, University of Barcelona

Av. Diagonal 690, Tower IV, Faculty of Economics and Business, Barcelona, Spain

a Corresponding author: egoya@ub.edu (+34) 93 402 19 84

This article analyses the impact that R&D expenditures and intra- and inter-industry externalities have on

the performance of Spanish firms. Despite the extensive literature studying the relationship between

innovation and productivity, there are far fewer studies in this particular area examining the importance

of sectoral externalities, especially focused on Spain. One novelty of this study, conducted for the

industrial and service sectors, is that we also consider the technology level of the sector in which the firm

operates and firm size. The database used is the Technological Innovation Panel (PITEC). It comprises

9,985 firms over the period 2004-2009 and has been used infrequently for studies of this type. The Olley

and Pakes (1996) estimator is adopted in order to account for both simultaneity and selection biases

providing consistent estimates. The results suggest that, unlike previous studies, R&D expenditures do

not have a direct impact on firm performance. By contrast, spillovers do. In particular, intra-industry

externalities present a positive and significant effect in low-tech and large firms. Inter-industry

externalities, however, present an ambiguous effect and there appears to be no specific pattern of

behaviour associated with technology level or firm size.

Keywords: Firm performance, innovation, sectoral externalities, firm size

JEL classification: D24, O33

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1. Introduction

It is widely acknowledged that if a firm can increase its productivity it is likely to gain in

competitiveness, an essential attribute in today’s globalized world. This is of particular relevance

in the case of Spain, because even though productivity has increased in recent years (Figure 1),

this growth is largely attributable to a drastic reduction in employment (see Table 1).

Fig.1 Real labour productivity (annual growth rate).

Source: Eurostat; own representation.

Table 1. Real GDP and employment (annual growth rates)

GDP growth Employment growth

Spain EU - 27 EU - 15 Spain EU - 27 EU - 15

2004 3.3 2.5 2.4 3.9 0.8 0.9

2005 3.6 2.1 1.9 5.6 1.5 1.6

2006 4.1 3.3 3.1 4.1 2.0 1.8

2007 3.5 3.2 3.0 3.1 1.9 1.8

2008 0.9 0.3 0.0 -0.5 1.1 1.0

2009 -3.7 -4.3 -4.3 -6.8 -1.7 -1.8

2010 -0.3 2.1 2.1 -2.3 -0.5 -0.4

2011 0.4 1.5 1.4 -1.9 0.4 0.4

Source: Eurostat

As shown in Figure 1, up to 2007, Spanish firms presented lower rates of labour productivity

(defined as the ratio between GDP and number of employees) growth than those recorded by

their European counterparts (both EU-27 and EU-15). Since that date, however, Spain’s growth

rates have risen notably taking them to the top of Europe’s rankings, with the exception of 2010.

This might suggest successful adaptation to the economic crisis; but, the reality is quite different.

As can be seen in Table 1, Spain’s GDP growth rate has been similar to or even lower than

Europe’s in recent years, while the fall in employment in Spain has been much greater than that

recorded in the rest of the continent since 2008. As a result, Spanish firms need to take steps

towards increasing their productivity by raising production, as opposed to via the destruction of

jobs.

In recent decades, the number of studies examining the relationship between innovation and

firm productivity has increased (see following section for a literature review). In general, the

3

findings stress the importance of R&D as a determinant of economic performance. Yet, as Figure

2 highlights, Spain’s business R&D to GDP ratio is much lower than that of the European Union

and, moreover, since 2008 it has gone into decline.

Fig.2 Business sector expenditure on R&D (% of GDP ratio).

Source: Eurostat; own representation.

Therefore, examining Spain’s R&D-productivity relationship in greater depth is essential if we

are to further our understanding of it and if we hope to design policies that can raise

productivity, especially in the current economic climate. For this reason, the primary goal of this

paper is to analyse the relationship between R&D and firm performance in Spain over the last

few years. In the light of previous studies that report differences in the productivity gains

attributable to innovation in accordance with a firm’s level of technology and size, and given

that few studies of the Spanish case take these two factors into account, here we assess whether

differences can be found between high- and low-tech firms, as well as between small and large

firms1.

Additionally, it should be borne in mind that the knowledge derived from a firm’s investment in

innovation is likely to spill over, given its inability to reap all the benefits from its investment.

Therefore, when examining the impact of innovation on productivity, the diffusion of the

innovation and any externalities generated also need to be taken into account. Several papers

have analysed the importance of spillovers; however, there has been little discussion about this

aspect from a sectoral perspective, particularly in Spain. Thus, the second goal of this paper is

to study the extent to which a firm’s performance is influenced by the innovation carried out by

other firms in the same sector (intra-industry externality) or by the innovation activities of firms

in other sectors (inter-industry externality).

1 It should be mentioned that Goya et al. (2012) carry out a similar analysis; however it is a much basic

work using cross-section data for 2010. In the present paper we use panel data and we are able to capture

changes over time as well as tackle several problems that appear when a productivity analysis is

undertaken. Unobserved heterogeneity or simultaneity issues need to be taking into account since they

might be affecting the relationship between innovation and productivity. The estimation method used

here (Olley and Pakes, 1996) account for these problems providing consistent results.

0,00

0,20

0,40

0,60

0,80

1,00

1,20

1,40

2005 2006 2007 2008 2009 2010

EU-27 EU-15 Spain

4

The study is conducted using the Technological Innovation Panel (PITEC) database, an

unbalanced panel of 9,985 Spanish firms from both the industrial and service sectors for the

period 2004 to 2009. It should be stressed that the study breaks new ground, since not only

examines the situation for the whole country but also for the industrial and service sectors. By

doing so, it aims to overcome a severe limitation given that most studies to date have focused

solely on the manufacturing sector or have covered only a specific region (as we shall see below).

In addition, PITEC contains a high level of sectoral information which enables us to focus our

attention on inter-industry externalities as well. Finally, the Olley and Pakes (1996) estimator is

adopted in order to account for unobserved heterogeneity, simultaneity issues and selection

bias. These are common problems that arise when a productivity analysis is carried out.

However, by using this method, consistent and reliable coefficients can be obtained.

To sum up, the aim of this paper is twofold. First, it seeks to analyse the extent to which the

technology level2 and size of Spanish firms affect the impact that R&D expenditures might have

on firm performance over the period 2004-2009. In other words, it investigates if there are

differences regarding the contribution of R&D in firm performance between low-tech and high-

tech firms as well as between small and large firms. Second, it assesses how these factors

influence potential knowledge flows from other firms’ innovations, both intra- and inter-

industry externalities. Thus, this article aims to answer the following questions: (i) Does the

impact of R&D on firm performance differ according to a firm’s technology level and/or firm

size? (ii) Are Spanish firms able to benefit from externalities? (iii) And if so, do these benefits

vary according to a firm’s technology level and/or firm size?

This paper has been organized as follows. Section 2 examines the previous literature, Section 3

presents the theoretical model, Section 4 describes the database and empirical model, Section

5 presents the results, and finally the conclusions are drawn in Section 6.

2. Previous Literature

A considerable body of literature has been published since Griliches (1979, 1986) first examined

the link between innovation and productivity. The well-known Cobb-Douglas production

function is normally used to conduct the empirical analysis, with the traditional inputs of physical

capital and labour being extended to include innovation expenditures. In general, the evidence

reveals a positive and significant relationship between innovation and productivity at the firm

level (see Mairesse and Sassenou 1991 for a detailed study, and also – to name but a few – Hall

and Mairesse 1995 for France; Harhoff 1998 for Germany; Lotti and Santarelli 2001 for a

comparative study of Germany and Italy; Parisi et al. 2006 for Italy and Ballot et al. 2006 for

France and Sweden). However, the results obtained seem to depend on the geographical area

being analysed as well as on the nature of the database and methodology used.

2 By technology level we mean the level of technology of the sector in which the firm operates. As it will

be seen in section 4.1 firms can be classified as high-tech manufacturing industries (HTMI), low-tech

manufacturing industries (LTMI), knowledge-intensive services (KIS) and non-knowledge intensive

services (NKIS). This is an interesting factor to include in the analysis as technological opportunities and

appropriability conditions are different between sectors which can lead to differences in the influence of

R&D. According to previous studies (see next section), investments in R&D carried out by firms operating

in “more advanced” sectors (HTMI and KIS) are more fruitful in increasing firm´s productivity than

investments undertake by firms operating in “less advanced” sectors (LTMI and NKIS).

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It is worth mentioning that most of these articles undertake cross-country analyses, and pay

scant attention to the impact that a firm’s sector or size might have. As stressed in the previous

section, our primary goal is to determine whether there are any differences in the impact of

innovation on firm performance according to these factors. Empirical evidence to date suggests

that the impact of R&D expenditures on a firm’s productivity is more marked in high-tech sectors

than it is in their low-tech counterparts (see Verspagen 1995 for nine OECD countries; Tsai and

Wang 2004 for Taiwan; Ortega-Argilés et al. 2010 and 2011 for European firms). As regards firm

size, we are interested in determining whether size influences the returns firms obtain from

innovation, taking into consideration that the larger the firm, the more innovation it is likely to

conduct (see Huergo and Jaumandreu 2004). However, very little research has been published

in the literature on this question. According to Castany et al. (2009), the size of Spanish firms

does have an influence on the returns obtained from investment in both innovation and human

capital, with the largest firms being the ones that benefit most from these investments.

In addition, this article seeks to determine if the stock of knowledge available at the firm level is

dependent on both the firm’s own innovation or on externalities. As discussed above, the

benefits derived from innovation in a firm (or sector) are likely to spill over because of the firm’s

inability to channel all of the benefits obtained from its investment effort. Thus, a firm’s

performance can be explained by its own knowledge as well as by the knowledge generated

somewhere else which is in the public domain. For this reason, externalities need to be taken

into consideration.

From an empirical perspective, most articles employ a production function where spillovers are

included as an additional input (following Griliches 1979). Although there has been a

considerable number of studies that have analysed the impact of R&D spillovers on productivity,

a general consensus has yet to be reached on just what that effect might be. Despite the positive

impact reported by some authors (Griliches 1992; Nadiri 1993; Cincera 2005; Wieser 2005; Aiello

and Cardamone 2005 and 2008; Cardamore 2012 and Bloch 2013), others draw different

conclusions (see, for example, Klette 1994 for Norway; Los and Verspagen 2000 for U.S. firms;

Harhoff 2000 for Germany; Wakelin 2001 for the United Kingdom; Rogers 2010 for United

Kingdom; Medda and Piga 2014 for Italy). Overall, the evidence is unclear, with spillovers having

an apparently heterogeneous effect (being positive, negative or not significant). Yet, it should

be borne in mind that the results are conditioned by the sector or country under analysis and

that they are highly dependent on the way in which externalities are quantified (using

technological similarity, geographical distance or commercial relations).

As far as Spain is concerned, the relationship between innovation and productivity has been

examined by a number of authors, who conclude that innovation has a positive impact on

productivity. However, a general limitation of most of these studies is that their analyses are

restricted to manufacturing firms based on the Encuesta sobre Estrategias Empresariales

dataset (ESEE)3. Among the most recent papers employing this database we find Vivero (2002),

Huergo and Moreno (2004), Huergo and Jaumandreu (2004), Maté-García and Rodríguez-

Fernández (2002, 2008), Rodríguez-Fernández and Maté-García (2006), Rochina-Barrachina et

al. (2010) and Casiman et al. (2010), to name but a few. By contrast, only a few papers have

carried out a joint analysis of both manufacturing and service sectors, most notably Segarra-

3 The ESEE is a firm-level survey of Spanish manufacturing which collects annual information since 1990.

6

Blasco (2010) and Segarra-Blasco and Teruel (2011) who analyse the case of Catalonia using data

from the fourth Community Innovation Survey (CIS4).

In the case of external knowledge, far too little attention has been paid to the effect of spillovers

on the productivity of Spanish firms. Some articles, including Ornaghi (2006), argue that

externalities are positive and significant in explaining productivity. In this study the impact of

spillovers on productivity growth is examined in 3,151 manufacturing firms from 1990 to 1999.

Spillovers are measured by taking into account firm size and the results indicate a positive impact

of technological externalities on growth in productivity. However, the results in other studies

differ according to the economic sector in which the firm operates and its level of technology.

For instance, Beneito (2001) analyses the impact of intra-industry externalities on productivity

for 501 Spanish manufacturing firms distinguishing them according to technology level during

the period 1990-96. The author concludes that only firms using advanced technologies are able

to benefit from the knowledge in their technological neighbourhood. In particular, the spillover

effect depends on the intensity of R&D, with firms in the intermediate quartile being the ones

that experience the largest productivity gains.

All in all, the question that still needs to be addressed is whether differences are to be found in

the returns that Spanish firms obtain from their innovation investments in relation to their

technology level or firm size. In addition, given the paucity of studies assessing the impact of

spillovers in Spain, this paper will examine if firms in the sample are able to benefit from the

innovation carried out by others.

3. Economic model

The model adopted to estimate the relationship between innovation and firm performance is

the extended Cobb-Douglas production function, which apart from including conventional

production factors (physical capital and labour) also incorporates human capital and

innovation4. In line with previous studies5, it is believed that the more qualified workers the firm

has, the more effectively they are likely to perform their tasks and the more productive the firm

is likely to be. Additionally, innovation, the variable of interest in this study, has been included

as a production input. Investment in innovation can reduce costs of production or raise firm

sales, thus increasing production.

𝑌𝑖𝑠𝑡 = 𝐴𝑖𝑠𝑡 ∙ 𝐾𝑖𝑠𝑡𝛽1 ∙ 𝐿𝑖𝑠𝑡

𝛽2 ∙ 𝐻𝑖𝑠𝑡𝛽3 ∙ 𝐼𝑖𝑠𝑡

𝛽4 [1]

where 𝑌𝑖𝑠𝑡 is the output of firm i belonging to sector s at year t, 𝐴𝑖𝑠𝑡 is the firm's technology

level, 𝐾𝑖𝑠𝑡 is physical capital, 𝐿𝑖𝑠𝑡 is labour, 𝐻𝑖𝑠𝑡 is human capital, 𝐼𝑖𝑠𝑡 is innovation and

𝛽1, 𝛽2, 𝛽3, 𝛽4 are the returns on each variable respectively.

Following Goya et al. (2012), it is assumed that firm's level of technology is dependent on the

innovation made by all the other firms:

4 It should be pointed out that only a few microeconomic studies, especially for the case of Spain, have

incorporated human capital and innovation as factors in the production function.

5 See for instance, Black and Lynch (1996) and Haltiwanger et al. (1999) for the United States, Turcotte

and Rennison (2004) for Canada, Arvanitis and Loukis (2009) for Greece and Switzerland, Yang et al. (2010)

for China and Lee (2011) for Malaysia.

7

𝐴𝑖𝑠𝑡 = 𝐴 ∙ (𝑆𝑖𝑠𝑡𝑖𝑛𝑡𝑟𝑎 )𝜙1 ∙ (𝑆𝑖𝑠𝑡

𝑖𝑛𝑡𝑒𝑟)𝜙2 [2]

where A is a constant denoting a common technology level for all the firms; 𝑆𝑖𝑠𝑡𝑖𝑛𝑡𝑟𝑎 is the intra-

industry externality of firm i in sector s capturing the innovative effort made by all the other

firms in the same sector; and 𝑆𝑖𝑠𝑡𝑖𝑛𝑡𝑒𝑟 is the inter-industry externality understood as the

innovation made by the firms in the rest of the sectors.

Thus by combining Equations [1] and [2] it can be seen that a firm's output is explained in terms

of its own investments (in physical capital, human capital and innovation) and of the knowledge

generated outside the firm:

𝑌𝑖𝑠𝑡 = 𝐴 ∙ 𝐾𝑖𝑠𝑡𝛽1 ∙ 𝐿𝑖𝑠𝑡

𝛽2 ∙ 𝐻𝑖𝑠𝑡𝛽3 ∙ 𝐼𝑖𝑠𝑡

𝛽4 ∙ (𝑆𝑖𝑠𝑡𝑖𝑛𝑡𝑟𝑎 )𝜙1 ∙ (𝑆𝑖𝑠𝑡

𝑖𝑛𝑡𝑒𝑟)𝜙2 [3]

Therefore, under the assumption that 𝜙1 ≠ 0 and 𝜙2 ≠ 0, even though a firm does not invest

in innovation, it can still benefit from the innovation carried out by all the other firms and,

thereby, increase its performance.

4. Data and empirical model

4.1. Data: Technology Innovation Panel (PITEC)

In the empirical analysis we use PITEC, which provides information on the innovation activities

of Spanish firms for the period 2003-20096. The National Institute of Statistics (INE), in

consultation with a group of experts and under the sponsorship of the Spanish Foundation for

Science and Technology (FECYT) and the Foundation for Technological Innovation (COTEC), is

responsible for building up this database. PITEC is built upon the Spanish Innovation Survey

carried out by the INE, which in turn is based on the Community Innovation Survey (CIS) which

follows guidelines laid down by the OECD's Oslo Manual and, through the use of a standardized

questionnaire, enables comparisons to be made between countries.

PITEC is a data panel based on a representative selection of firms, which makes it possible to

carry out repeated observations of the economic units included over time and thereby develop

much more precise estimations of the evolution of R+D+I activities in the business sector

(innovation expenditures, composition of the samples, etc.), determine the impact of innovation

(different effects on productivity) and identify the various strategies in the decisions adopted by

firms when introducing innovations into their business (for instance the different compositions

of internal and external R&D expenditures as a part of total expenditures). The panel is made up

of four non-excludable samples: (i) firms with 200 or more employees, (ii) firms with internal

R&D expenditures, (iii) firms with fewer than 200 employees with external R&D expenditures

but which carry out no internal R&D, and (iv) firms with fewer than 200 employees with no

innovation expenditures.

6 Last year available when this paper was written.

8

Although PITEC has provided statistical information since 2003, in this paper we only draw on

data for the period 2004-20097. After a filtering process8, only firms with ten or more employees9

operating in the industrial and service sectors were selected (primary sector and construction

were thus excluded). Note that the influence of extreme outliers in the sample was treated

accordingly (see appendix A). Our eventual sample consisted of an unbalanced panel of 9,985

firms (51,604 observations).

PITEC provides information on individual firm characteristics (sales, exports, employment, the

market in which a firm operates, industry sector, etc.) along with detailed information on

innovation activities, such as different types of innovation expenditures, cooperation between

firms, barriers to innovation and number of patents, etc.

There is a double advantage of using this database. Firstly it provides information on both the

industrial and service sectors, making possible to overcome the limitation that most studies in

Spain focus solely on the manufacturing sector, generally using the ESEE. Secondly, it contains a

high level of sectoral information covering 51 sectors (see appendix B). This level of detail

enables a rich study to be undertaken examining differences in behaviour between sectors with

different technology levels and, in turn, making a more interesting study of inter-industry

externalities possible.

In line with the aim of this paper and for the purposes of analysing whether the impact of

innovation and externalities on firms performance vary depending on the sector's technology

and firm size, the total sample of 9,985 firms has been divided up according to the technology

level of the sector in which the firm operates and also according to firm size. In the first case, we

have used the Eurostat classification and grouped the firms by sector into the following

categories: (a) low and medium-low tech manufacturing firms (LTMI), (b) medium-high and high-

tech manufacturing firms (HTMI), (c) non-knowledge-intensive services (NKIS) and (d)

knowledge-intensive services (KIS). In the second case, we have distinguished between: (a) small

firms (from 10 to 49 employees), (b) medium-sized firms (from 50 to 199 employees) and (c)

large firms (200 or more employees). To see the distribution of firms according to sub-samples

see appendix C.

4.2. Empirical model and estimation issues

According to the expression [3], and using the information supplied by the PITEC database10,

the following econometric model can be specified:

7 We do not use data for 2003 because of its severe limitations. PITEC began with just two samples in 2003

(a sample of firms with 200 or more employees and a sample of firms with internal R&D expenditures). In

2004, it overcame this limitation by including a sample of firms with fewer than 200 employees with

external R&D expenditures but which carried out no internal R&D and a sample of firms with fewer than

200 employees with no innovation expenditures.

8 This filtering process meant eliminating those observations that included any kind of ‘incident’ (i.e.,

confidentiality problems, takeovers, mergers, etc.) and those containing obvious anomalies (such as null

sales).

9 The population area is as defined by the Spanish Innovation Survey on which PITEC is based.

10 Unfortunately, PITEC does not contain information about intermediate consumptions, prices or other

information necessary to compute total factor productivity (or multi-factor productivity). Thereby, this

9

𝑦𝑖𝑠𝑡 = 𝛽0 + 𝛽1𝑘𝑖𝑠𝑡 + 𝛽2𝑙𝑖𝑠𝑡 + 𝛽3ℎ𝑖𝑠𝑡 + 𝛽4𝑖𝑖𝑠𝑡 + 𝜙1𝑆𝑖𝑠𝑡−2𝑖𝑛𝑡𝑟𝑎 + 𝜙2𝑆𝑖𝑠𝑡−2

𝑖𝑛𝑡𝑒𝑟 + 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝑢𝑖𝑠𝑡 [4]

𝑢𝑖𝑠𝑡 = ω𝑖𝑠𝑡 + 휀𝑖𝑠𝑡 [5]

where 𝑦𝑖𝑠𝑡 approximates the sales, 𝑘𝑖𝑠𝑡 is the physical capital stock, 𝑙𝑖𝑠𝑡 is the number of

employees, ℎ𝑖𝑠𝑡 is the percentage of employees with higher education, and 𝑖𝑖𝑠𝑡 is defined as

R&D stock11. 𝑆𝑖𝑠𝑡𝑖𝑛𝑡𝑟𝑎 and 𝑆𝑖𝑠𝑡

𝑖𝑛𝑡𝑒𝑟 are the externalities defined below. The regression controls are

represented by technology level and firm size dummies and also a trend to capture time

changes12. On the other hand, 𝜔𝑖𝑠𝑡 is the productivity shock and 휀𝑖𝑠𝑡 is the error term (which can

contain unpredictable productivity shocks). Lower case letters indicates log-transformed

variables (with the exception of human capital which is a percentage). All monetary variables

are expressed at constant values at 2009 base prices (nominal values have been deflated using

the GDP deflator).

As is widely accepted in the literature13, a firm’s output is assumed to be affected by

accumulated stocks of physical capital and R&D expenditures, rather than by current flows. We

use the well-known perpetual inventory method:

𝐾𝑡 = 𝐾𝑡−1 ∙ (1 − 𝛿ℎ𝑘) + 𝐶𝑡

𝐾0 =𝐶0

𝑔𝑠𝑘+𝛿ℎ

𝑘

[6]

and

𝐼𝑡 = 𝐼𝑡−1 ∙ (1 − 𝛿ℎ𝑖 ) + 𝑅𝐷𝑡

𝐼0 =𝑅𝐷0

𝑔𝑠𝑖+𝛿ℎ

𝑖

[7]

with t = 2004, …, 2009 h = 1, 2, 3, 4 s = 1, ..., 51.

where 𝐶𝑡 is the real investment in material goods and 𝑅𝐷𝑡 are the real R&D expenditures. We

applied different depreciation rates according to the technology level (h). Following Ortega-

Argilés (2011), the more advanced the sector, the faster is the technological progress

accelerating the obsolescence of its current physical capital and knowledge. Thus, we applied

sectoral depreciation rates of 6% and 7% for physical capital (𝛿ℎ𝑘) and 15% and 18% for

innovation (𝛿ℎ𝑖 ) to low-tech and high-tech sectors respectively14. In the case of growth rates, if

we used the initial periods for their computation, we would lose a considerable amount of

kind of analysis is not possible here. The only measure available regarding output is firm sales (this is

something common when Innovation Surveys are used, for instance Community Innovation Survey).

11 We are aware that the concept of innovation is very wide including not only R&D expenditures, but

also, the acquisition of machinery, equipment and hardware/software, training staff directly involved in

developing the innovation, introduction innovation in the market, design, etc. However, in line with

previous studies, we approximate innovation with R&D expenditures (both internal and external).

12 Although industry dummies would capture technological opportunities as well as specificities of the

sector, we cannot include them since it would give rise to perfect multicollinearity with the inter-industry

externalities.

13 See Hall and Mairesse (1995), Bönte (2003) and Ortega-Argilés et al. (2011) to name just a few.

14 The results are almost identical if a fix depreciation rate is used (6% for physical capital and 15% for

innovation).

10

information given that our panel has a short time dimension (2004-2009). Thus, we opted to

calculate 𝑔𝑠𝑘 and 𝑔𝑠

𝑖 as the average rate of change in real investment in material goods and real

R&D expenditures in each sector(s) over the period 1995-200315. We used the OECD’s ANBERD

database to calculate physical capital growth rates (𝑔𝑠𝑘) and the OECD’s STAN database for

innovation growth rates(𝑔𝑠𝑖 ).

As regards externalities, due to the many different ways in which spillovers can occur (learning

what other firms do either via the movements of workers themselves or through reading articles

in journals, attending conferences, disclosure of patents, reverse-engineering, etc.), measuring

them is a far from easy task. We follow the definition adopted by Goya, et al. (2012) where the

authors use R&D expenditures to proxy external knowledge.

Thus, intra-industry externality is defined as:

𝑆𝑖𝑠𝑡𝑖𝑛𝑡𝑟𝑎 = ∑ 𝐼𝑗𝑠𝑡𝑗≠𝑖 [8]

where 𝐼𝑗𝑠𝑡 is total R&D stock carried out in sector s at time t (except the firm’s own R&D

investment). This definition is open to criticism since it attaches the same weight to all the firms

in the same sector. Thus, it assumes that the propensity to benefit from the R&D expenditures

in the sector is the same for all firms. However, by employing this definition, the technological

effort of the sector in which the firm is located is captured and so it serves as an indicator of the

magnitude of the technological effort currently available in the sector.

On the other hand, it can be supposed that firms which maintain commercial relationships

benefit from the knowledge embodied in the products in which they trade. Thus, the more they

buy and sell, the more they benefit from the knowledge originated in the other sector.

Therefore, inter-industry externality is defined in the following way:

𝑆𝑖𝑠𝑡𝑖𝑛𝑡𝑒𝑟 = ∑ 𝑤𝑠𝑚 ∙ 𝐼𝑗𝑚𝑡𝑚≠𝑠 [9]

where 𝐼𝑗𝑚𝑡 is R&D stock undertaken in all the other sectors and 𝑤𝑠𝑚 is defined as the quotient

between the intermediate purchase by sector s of goods and services supplied by sector m and

the total sum of intermediate purchase of sector s. Thus, the influence that the R&D

expenditures of firms in sector m have on the productivity of firm i in sector s is based on the

relative importance that sector m has as supplier to sector s. To construct the weights we use

the symmetric input-output table for Spain for 2005 (the latest year available). An exercise of

correspondence has had to be carried out between the branches of business activity according

to which PITEC data are classified and the branches of business activity in the input-output table.

As it has been seen in the literature review, both positive and negative coefficients are possible.

On the one hand, an external pool of knowledge is expected to have a positive impact on the

production of other firms, since they can benefit from new knowledge and ideas. However, on

the other hand, if rival firms increase their R&D investment a competitive effect might appear.

This is what some authors have called “market-stealing effect” (De Bondt, 1996; Bitzer and

15 Note, however, that the choice of g does not modify the results greatly. As Hall and Mairesse (1995)

report in footnote 9: “In any case, the precise choice of growth rate affects only the initial stock, and

declines in importance as time passes…”.

11

Geishecker, 2006; Bloom et al., 2013). Therefore, both possibilities need to be taken into

consideration.

As can be seen in expression [4], both externalities have been lagged two periods, since the

assumption of a contemporary relationship between them and firm performance seems

inappropriate, given that there is no immediate impact. On the contrary, it is likely that the

diffusion of external knowledge takes some time before it can affect the firm.

As far as the estimation method is concerned, it is well-known in the literature that estimates

from a production function may suffer from simultaneity as well as selection bias. As such, OLS

estimates are biased and inconsistent.

The former problem, simultaneity (first noted by Marschak and Andrews, 1944), arises because

productivity shocks (𝜔𝑖𝑠𝑡) are known to the firm but not to the econometrician. Thus, when the

firm has to determine the usage of inputs its decision is influenced by its beliefs about

productivity. Therefore, input choices will be correlated with productivity shocks. So if a firm

faces a positive (negative) productivity shock, its use of inputs is going to increase (decrease)

accordingly. Not taking this issue into consideration leads to biased and inconsistent estimates

since the error term is correlated with the explanatory variables (breaking one of the basic

hypotheses of OLS). The second problem, selection bias, manifests itself because of the exit of

inefficient firms (not randomly). A firm with higher capital is expected to obtain higher profits in

the future and so is more likely to stay in the market despite a negative productivity shock (given

that in the future it is thought it will produce more) than a firm with lower capital16.

In order to deal with these problems and to ensure the reliability of the coefficients we use the

estimator proposed by Olley and Pakes in 1996 (hereafter OP). Other methods are employed in

the literature, but they only address simultaneity bias17. In contrast, the OP estimator addresses

both simultaneity as well as selection bias18. For this reason, this estimation method has been

used by several authors who seek to analyse, for instance, the impact of trade and tariffs on firm

productivity (see for example, Pavcnick, 2002; Schor, 2004; Amiti and Koungs, 2007; De Loecker,

2007; Loung, 2011; Van Beveren, 2012 and Arvas and Uyar, 2014). While it is true that it is not

one of the most common approaches in the innovation literature, some papers have recently

applied this estimator to obtain unbiased and consistent estimates of the production function

(see Marrocu, et al.; 2011 and Añón-Higón and Manjón-Antonlín, 2012).

16 Even though the evidence suggests that simultaneity bias is more important than selection bias, the estimator used here controls for both.

17 As shown in the literature, simultaneity problems can be addressed by using instrumental variables or fixed effect estimators. However, given that the panel is short and instrumental variables use lagged values as instruments, their suitability is reduced (facing a problem of weak instruments). The fixed effects estimator, on the other hand, relies on the assumption that productivity shocks are constant over time, which is an excessively strong premise from our point of view, especially bearing in mind the economic situation in the period under analysis.

18 Another option might be the Levinsohn and Petrin (2003) estimator, but intermediate inputs are needed in this case (and this information is not provided by PITEC). Thus, we strongly believe that the best option available is the OP estimator.

12

4.2.1. Olley & Pakes (1996) estimator

Intuitively, the idea behind the OP method is to make “observable” the “unobservable”; in other

words, to use an equation to proxy for the unobserved productivity shock. By doing so, the

econometrician can control for the correlation between the error term and the inputs and

obtain consistent results. Below, the main features of the OP approach are described (using the

same notation as that employed by Olley and Pakes, 1996)19. After that, the changes introduced

in order to adapt the method to our case are explained.

First of all, Olley and Pakes base their analysis on the traditional Cobb-Douglass production

function:

𝑦𝑖𝑡 = 𝛽0 + 𝛽𝑎𝑎𝑖𝑡 + 𝛽𝑘𝑘𝑖𝑡 + 𝛽𝑙𝑙𝑖𝑡 + 𝑒𝑖𝑡 [10]

where the log of output (𝑦𝑖𝑡) is explained by the age of the firm (𝑎𝑖𝑡), the log of capital (𝑘𝑖𝑡), the

log of labour (𝑙𝑖𝑡) and an error term (𝑒𝑖𝑡) that is decomposed in two components: a productivity

shock (𝜔𝑖𝑡) and a measurement error term or unexpected productivity shock (𝜂𝑖𝑡). The former

is observed by the firm but not by the econometrician and affects the firm’s decision, while the

latter is unobserved by both and does not affect the firm’s decision.

As for the inputs in the production function, the authors differentiate between variable factors

(such as, 𝑙𝑖𝑡), which can be easily adjusted given a productivity shock, and fixed factors (also

known as “state variable”) which depend on information from the previous period and are costly

to adjust with productivity shocks (𝑘𝑖𝑡 and 𝑎𝑖𝑡).

As explained by the authors, at the beginning of every period an incumbent firm has to decide

whether to exit or to continue in the market. The firm remains in the market if the current state

variables indicate that the returns of staying in the business are higher than the sell-off value.

This is known as the “exit rule” and is specified as follows:

𝜒𝑡 = {1 𝑖𝑓 𝜔 ≥ 𝜔(𝑎𝑡 , 𝑘𝑡)

0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 [11]

where 𝜒𝑡 is an indicator function that takes a value of 1 if the firm remains in the market and 0

if the firm exits and 𝜔 is a productivity threshold that depends on the firm’s capital and age.

If the firm continues in the market, it chooses a level of inputs (𝑙𝑖𝑡) and a level of investment

according to 𝜔𝑖𝑡 , 𝑘𝑖𝑡 and 𝑎𝑖𝑡. This is known as the “investment decision rule”:

𝑖𝑖𝑡 = 𝑖( 𝜔𝑖𝑡 , 𝑘𝑖𝑡 , 𝑎𝑖𝑡) [12]

Provided that 𝑖 > 0 and investment is increasing in productivity, the investment function can be

inverted as follows: 𝜔 = ℎ( 𝑖𝑖𝑡 , 𝑘𝑖𝑡 , 𝑎𝑖𝑡) where ℎ(∙) = 𝑖−1(∙). As the authors put it “[this

expression] allows us to express the unobservable productivity variable, 𝜔𝑡 , as a function of

observables, and hence to control for 𝜔𝑡 in the estimation” (Olley and Pakes, 1996 page 1275).

The production function can then be rewritten as:

𝑦𝑖𝑡 = 𝛽𝑙𝑙𝑖𝑡 + 𝜙( 𝑖𝑖𝑡 , 𝑘𝑖𝑡 , 𝑎𝑖𝑡) + 𝜂𝑖𝑡 [13]

19 For a detailed explanation of the equations and estimation strategy see Olley and Pakes (1996).

13

where 𝜙( 𝑖𝑖𝑡 , 𝑘𝑖𝑡 , 𝑎𝑖𝑡) = 𝛽0 + 𝛽𝑎𝑎𝑖𝑡 + 𝛽𝑘𝑘𝑖𝑡 + ℎ( 𝑖𝑖𝑡, 𝑘𝑖𝑡 , 𝑎𝑖𝑡) and is approximated by a third-

order polynomial series in age, capital and investment20. Equation [13] can be consistently

estimated by OLS since 𝜙(∙) controls for unobserved productivity and, therefore, the inputs are

no longer correlated with the error term.

From Equation [13] we can identify 𝛽𝑙 but not 𝛽𝑎 or 𝛽𝑘. To do so, estimates of the survival

probabilities are needed. The following probit model is fitted:

Pr{𝜒𝑡 = 1|𝜔(𝑘𝑡 , 𝑎𝑡), 𝐽𝑡−1} = 𝑃𝑡 [14]

where 𝑃𝑡 is the probability of the firm surviving in the market and 𝐽𝑡−1 is the information

available at time t-1. To compute these probabilities the exit rule defined in Equation [11] is

employed (𝜒𝑡 = 1). Thus, the probability of survival in period t depends on age, capital and

investment in t-1 using the same third-order polynomial as before.

Finally, estimated coefficients of age (𝛽𝑎) and capital (𝛽𝑘) are obtained running non-linear least

squares using �̂�𝑙, �̂� and �̂�:

𝑦𝑖𝑡 − �̂�𝑙𝑙𝑖𝑡 = 𝛽𝑎𝑎𝑖𝑡 + 𝛽𝑘𝑘𝑖𝑡 + 𝑔(�̂�𝑖𝑡 , �̂�𝑡−1 − 𝛽𝑎𝑎𝑖𝑡−1 − 𝛽𝑘𝑘𝑖𝑡−1) + 𝜉𝑖𝑡 + 𝜂𝑖𝑡 [15]

where 𝑔(∙) accounts for the selection bias by using a third-order polynomial in �̂�𝑖𝑡 and �̂�𝑡−1 −

𝛽𝑎𝑎𝑖𝑡−1 − 𝛽𝑘𝑘𝑖𝑡−1. The function 𝑔(∙) is comparable to the inverse Mills ratio used in Heckman

type models.

To summarize, the OP methodology comprises three stages. The first one controls for

unobserved productivity shocks including the inverse of the investment demand function in the

estimation. Thus, as the error term is no longer correlated with the inputs, the simultaneity bias

can be removed. The second estimates the survival probabilities in order to address selection

bias. The third and final stage obtains the coefficients of the state variables (𝛽𝑎 and 𝛽𝑘) by

including the survival probabilities and the inputs estimates computed in the previous steps.

We modify this method slightly to adapt it to our specific requirements. First of all, age is not

included in the analysis since this information is not available for all firms21. Second, R&D

expenditures are incorporated in the analysis. We follow Amiti and Koungs (2007)22 and include

R&D stock as a state variable since treating it as exogenous is inappropriate. As with physical

capital, we assume that the decision to invest in R&D is taken in t-1. Thus, the investment

demand function depends on: 𝑖𝑖𝑡 = 𝑖( 𝜔𝑖𝑡 , 𝑘𝑖𝑡 , 𝑟𝑑𝑖𝑡). The rest of the procedure is the same as

described above, apart from the fact that we include 𝑟𝑑𝑖𝑡 as state variable instead of 𝑎𝑖𝑡.

5. Results

5.1. Descriptive statistics

Table 2 and Table 3 show the descriptive statistics for the main variables in the model across the

different technology levels and firm sizes.

20 Estimates are obtained using the “opreg” command in Stata (Yasar et al., 2008).

21 PITEC includes this information from 2009.

22 The authors modify the OP framework by introducing the decision to engage in international trade.

14

First, it can be seen that sales in manufacturing firms vary slightly with the level of technology.

However, in the case of the service sector, firms that operate in non-knowledge-intensive

services present higher sales than those reported by their counterparts operating in knowledge-

intensive services. In the case of capital stock, low-tech firms (LTMI and NKIS) show a somewhat

higher ratio than that recorded by high-tech firms (HTMI and KIS). As expected, firms belonging

to service sector are larger (higher number of employees) than manufacturing firms. As far as

human capital is concerned, the average percentage of qualified employees is much higher in

more advanced firms. Specifically, in knowledge-intensive services approximately 42% of

workers have completed higher education.

R&D stock is also greater in technologically advanced firms (in both the industrial and the service

sectors). In particular, firms belonging to non-knowledge-intensive services are the ones that

invest least in R&D. On the other hand, in both sectors, firms present higher capital stock than

R&D stock. This differential is most marked in low tech-firms (13.8 vs 10.3 in the industrial sector

and 13.6 vs 4.9 in the service sector). Thus, low-tech firms would appear to be much more capital

intensive than their high-tech counterparts. Finally, the intra-industry externality presents much

greater values in high-tech firms (HTMI and KIS), while inter-industry externality shows a more

uniform distribution.

In order to test whether there are differences in sales according to technology level, we

conducted a Kruskal-Wallis test23. The result clearly rejects the null hypothesis (p-value=0.0001)

of equal population medians24.

Table 2: Descriptive statistics by technology levela

LTMI HTMI NKIS KIS

Mean St.dev Mean St.dev Mean St.dev Mean St.dev

𝑦𝑖𝑠𝑡

overall 16.225 1.635 16.202 1.606 17.117 1.839 15.738 1.990

between 1.630 1.613 1.855 1.978

within 0.317 0.313 0.288 0.419

𝑘𝑖𝑠𝑡

overall 13.795 4.962 13.540 4.280 13.637 5.408 12.680 4.854

between 4.874 4.125 5.143 4.754

within 2.035 1.879 2.443 2.090

𝑙𝑖𝑠𝑡

overall 4.248 1.202 4.166 1.221 5.085 1.607 4.560 1.611

between 1.203 1.223 1.624 1.630

within 0.175 0.169 0.180 0.223

ℎ𝑖𝑠𝑡

overall 12.198 13.660 20.931 18.698 14.134 20.271 42.497 33.307

between 11.834 16.863 18.252 30.160

within 7.118 8.750 9.789 14.715

𝑖𝑖𝑠𝑡

overall 10.313 5.448 12.658 4.142 4.964 6.136 9.199 6.438

between 5.322 4.100 5.932 6.221

within 1.474 1.245 1.555 1.508

𝑆𝑖𝑠𝑡𝑖𝑛𝑡𝑟𝑎

overall 386.754 276.758 1215.540 671.917 422.577 474.082 762.791 884.113

between 267.802 657.445 406.578 852.951

23 The test was applied both to the whole sample and on a year-by-year basis.

24 The nonparametric Kruskal-Wallis test is a safer alternative than parametric tests in which there are

concerns about the normality assumptions or suspicions of outlier problems.

15

within 67.037 118.282 248.403 270.352

𝑆𝑖𝑠𝑡𝑖𝑛𝑡𝑒𝑟

overall 523.714 163.802 554.360 143.338 425.268 250.815 407.984 154.324

between 128.565 97.383 236.827 122.566

within 105.600 111.621 73.687 102.600

Firms 3,380 2,465 1,261 2,879

(%) (33.85) (24.69) (12.63) (28.83)

Observations 17,795 13,022 6,637 14,150

(%) (34.48) (25.23) (12.86) (27.42)

Notes: LTMI (low and medium-low tech manufacturing firms), HTMI (medium-high and high tech manufacturing

firms), KNIS (non-knowledge-intensive services), KIS (knowledge-intensive services). a Sales (𝑦𝑖𝑠𝑡), capital stock (𝑘𝑖𝑠𝑡),

labour (𝑙𝑖𝑠𝑡), and R&D stock (𝑖𝑖𝑠𝑡), are taken in logarithms, while human capital (ℎ𝑖𝑠𝑡) is a percentage Between

variation means variation across individuals and within variation means variation over time around individual mean.

Table 3 shows that the larger the firm, the greater sales it has and the more capital intensive it

is. By contrast, the larger the firm, the less human capital it has. As for R&D stock, medium-sized

firm are the ones that most invest in R&D, followed by small and large firms respectively. Finally,

the smaller the firm, the greater value of intra-industry externalities it presents, while in the

case of inter-industry externalities, the highest coefficient is for medium-sized firms, followed

by small and large companies.

We also tested whether median sales varies by firm size and, again, the Kruskal-Wallis test

rejects (p-value=0.0001) the null hypothesis of equal medians.

Table 3: Descriptive statistics by firm sizea

Small Medium Large

Mean St. dev Mean St. dev Mean St. dev

𝑦𝑖𝑠𝑡

overall 14.792 1.092 16.491 1.053 18.042 1.415

between 1.097 1.072 1.392

within 0.356 0.291 0.300

𝑘𝑖𝑠𝑡

overall 11.853 4.671 13.843 4.418 15.320 4.751

between 4.607 4.305 4.672

within 2.003 1.901 2.087

𝑙𝑖𝑠𝑡

overall 3.134 0.459 4.556 0.402 6.231 0.847

between 0.469 0.423 0.828

within 0.161 0.133 0.163

ℎ𝑖𝑠𝑡

overall 28.356 27.968 21.130 23.798 16.434 22.246

between 26.496 22.825 20.018

within 10.665 8.826 10.486

𝑖𝑖𝑠𝑡

overall 10.559 4.936 11.418 5.124 7.470 7.367

between 4.934 5.297 7.099

within 1.074 1.176 1.952

𝑆𝑖𝑠𝑡𝑖𝑛𝑡𝑟𝑎

overall 759.825 691.588 745.252 723.731 578.362 686.781

between 685.296 722.103 672.709

within 180.139 174.715 161.081

𝑆𝑖𝑠𝑡𝑖𝑛𝑡𝑒𝑟

overall 495.239 180.729 507.187 179.663 455.145 179.559

between 158.746 157.424 154.760

within 99.678 98.643 97.610

16

Firms 5,037 3,450 3,046

(%) (43.67) (29.91) (26.41)

Observations 22,372 14,403 14,829

(%) (43.35) (27.91) (28.74)

a Sales (𝑦𝑖𝑠𝑡), capital stock (𝑘𝑖𝑠𝑡), labour (𝑙𝑖𝑠𝑡), and R&D stock (𝑖𝑖𝑠𝑡), are taken in logarithms, while human capital

(ℎ𝑖𝑠𝑡) is a percentage. Between variation means variation across individuals and within variation means variation over

time around individual mean.

In addition, from Tables 2 and 3 it can be seen that between variation, i.e. variation across

individuals, is much higher than within variation, i.e. variation over time for each individual

(around individual mean).

Before showing the results, we present the density functions of the logarithm of sales according

to technology level and firm size.

17

Fig.3 Distribution of log (Sales) according to technology level

Source: PITEC; own representation.

In Figure 3 it can be seen that the density functions vary according to the level of technology

where the firm operates; however, no clear pattern can be drawn. Nevertheless, Figure 4 shows

clear differences in the density functions depending on the firm size. Thus, the density functions

move further right as the size increases. Therefore, small firms present a density function further

to the left on the graph, which indicates lower level of sales. This is followed by the medium-

sized firms and large firms that present, on average, higher sales, but they present greater

dispersion too.

Fig.4 Distribution of log (Sales) according to firm size

Source: PITEC; own representation.

Therefore, these graphs show that without taking other factors into account, firm sales are

distributed differently depending on the technology level or firm size. In particular, firm size has

a clear positive influence on firm sales as the density function moves rightwards towards greater

size, therefore reaching higher levels.

5.2. Estimates

In this section we present the results of the estimates of Equation [4]. For this we use the OP

estimator using the “opreg” command from Stata (Yasar et al. 2008) to account for the existence

0

.05

.1.1

5.2

.25

Den

sity

5 10 15 20 25ln(Sales)

LTMI

HTMI

NKIS

KIS

0.1

.2.3

.4

Den

sity

5 10 15 20 25ln(Sales)

Small

Medium

Large

18

of simultaneity and selection bias. To derive the OP estimator, we define physical capital stock

and R&D stock as “state variables” (since they are quasi-fixed inputs), we consider labour,

human capital and externalities as “free” inputs, the investment in both physical capital and R&D

is our “proxy variable” (or in other words, the variable that controls for unobserved productivity)

and finally, the remaining variables are controls. Table 4 presents the main results, while

intermediate results from the first and second stage of the model can be found in appendix D.

Table 4 shows the results of the estimation for the sample as a whole and for the sub-samples

(i.e., according to the technology level of the sector in which the firm operates and according to

the firm size). Our aim is to determine whether there are differences in the returns firms obtain

from their own R&D expenditures and those obtained from externalities.

First, when examining sample as a whole (column 1), capital stock (𝑘𝑖𝑠𝑡) has a positive impact

on firm performance. The same result can be observed when we disaggregate the sample by

technology level (columns 2 to 5), except for non-knowledge-intensive services. On the other

hand, firm size (columns 6 to 8) appears to have a positive influence, i.e., the larger the firm, the

more benefits it obtains from its physical capital stock. Second, the results presented in Table 4

show a positive elasticity regarding the number of employees (𝑙𝑖𝑠𝑡) and, in consonance with

previous research, our findings highlight the role played by human capital (ℎ𝑖𝑠𝑡) in all sub-

samples, especially in low-tech firms (both LTMI and NKIS). The breakdown by firm size

illustrates that human capital appears to increase their effect with this factor, thus the larger

the firm, the greater its impact.

In line with the literature, R&D stock (𝑖𝑖𝑠𝑡) has a positive impact on the sample as a whole.

However, once technology level or firm size is taken into account, this effect disappears.

Although this finding is not common in the literature, it is not as counterintuitive as it might

seem at first glance. In particular, this result is in line with the new approach introduced by

Crépon, Duguet and Mairesse (1998). In their seminal paper the authors argue that R&D

expenditures do not have a direct effect on firm productivity, or as the authors say “we explicitly

account for the fact that it is not innovation input (R&D) but innovation output that increases

productivity” (page 116). To sum up25, the idea is the following: it is the firm’s capacity to

generate innovations and its ability to translate innovations into economic performance, rather

than R&D investment itself, that might have an impact on the firm’s production. It is our belief

that this line of thinking is quite logical and, moreover, it helps shed light on the results obtained

here.

As for the intra-industry externalities, although very small, they present a negative coefficient in

the sample as a whole (column 1). Thus, if the rest of the firms in the same sector increase their

R&D expenditures, the production of the firm falls. Although if other firms invest in R&D, the

pool of knowledge available in the sector will rise, a competition effect may also appear as firms

might be rivals. This could compensate for, or even come to dominate (as in this case), the

benefits derived from the R&D investment of others, suggesting the existence of a “market-

stealing effect”.

Table 4 OP estimates. Estimation results of Equation [4]. 2004-2009. Dependent variable: ln(sales) Total LTMI HTMI NKIS KIS Small Medium Large

25 It should be borne in mind that the aim here is not to compare the two approaches, but rather to provide a plausible explanation for the results obtained.

19

(1) (2) (3) (4) (5) (6) (7) (8)

𝑘𝑖𝑠𝑡 0.0384*** 0.0269*** 0.0248*** 0.0279 0.0691*** 0.0317*** 0.0331** 0.0384***

(0.0081) (0.0080) (0.0076) (0.0183) (0.0165) (0.0087) (0.0154) (0.0125)

𝑙𝑖𝑠𝑡 0.8187*** 0.9235*** 0.9861*** 0.8117*** 0.7437*** 1.0445*** 0.9094*** 0.7024***

(0.0162) (0.0313) (0.0304) (0.0466) (0.0306) (0.0270) (0.0404) (0.0255)

ℎ𝑖𝑠𝑡 0.0030*** 0.0060*** 0.0026*** 0.0085*** 0.0019*** 0.0011** 0.0034*** 0.0070***

(0.0005) (0.0010) (0.0008) (0.0017) (0.0006) (0.0005) (0.0009) (0.0009)

𝑖𝑖𝑠𝑡 0.0119** 0.0115 -0.0026 0.0008 0.0170 0.0142 0.0385*** 0.0047

(0.0060) (0.0084) (0.0093) (0.0095) (0.0129) (0.0099) (0.0127) (0.0065)

𝑆𝑖𝑠𝑡−2𝑖𝑛𝑡𝑟𝑎 -0.0000** 0.0004*** 0.0000* 0.0010*** -0.0003*** -0.0001*** -0.0001*** 0.0001***

(0.0000) (0.0001) (0.0000) (0.0001) (0.0000) (0.0000) (0.0000) (0.0000)

𝑆𝑖𝑠𝑡−2𝑖𝑛𝑡𝑒𝑟 -0.0002*** -0.0001 0.0004** 0.0007*** -0.0012*** -0.0003*** -0.0001 -0.0002

(0.0001) (0.0001) (0.0002) (0.0001) (0.0002) (0.0001) (0.0001) (0.0002)

Trend -0.0118* -0.0364*** -0.0853*** -0.1905*** 0.1215*** -0.0045 -0.0175 -0.0222

(0.0063) (0.0091) (0.0175) (0.0155) (0.0169) (0.0097) (0.0116) (0.0141)

Medium 0.2168*** 0.1143** 0.0694* 0.3132*** 0.1913***

(0.0289) (0.0476) (0.0418) (0.1056) (0.0627)

Large 0.2035*** 0.0383 -0.0667 0.2901* 0.2287**

(0.0494) (0.0786) (0.0747) (0.1641) (0.1036)

HTI 0.0953*** 0.1595*** 0.1301*** -0.0879

(0.0254) (0.0373) (0.0428) (0.0598)

NKIS 0.1478*** 0.4437*** 0.3305*** -0.0766

(0.0402) (0.0458) (0.0618) (0.0566)

KIS -0.7088*** -0.5722*** -0.7679*** -0.7004***

(0.0300) (0.0479) (0.0677) (0.0560)

Firms 8,638 3,023 2,177 1,116 2,322 3,962 2,856 2,574

Obs 23,004 7,990 5,891 3,027 6,096 9,461 6,680 6,863

Note: Low and medium-low tech manufacturing firms (LTMI), medium-high and high tech manufacturing firms

(HTMI), non-knowledge-intensive services (NKIS), knowledge-intensive services (KIS). Externalities are lagged two

periods. Reference groups: LTMI and small firms. Bootstrapped errors in parenthesis.*** Significant at 1%, **

significant at 5%, * significant at 10%.

The breakdown by technology level illustrates that, in general, intra-industry externalities have

a positive effect on firm sales, except in the case of firms operating in knowledge-intensive

services that show negative return from intra-industry externalities. Although a negative

coefficient is not an “attractive” result, it is completely plausible since firms in this sector are

characterised by a higher degree of technology and this might give rise to a higher degree of

competition. In contrast, the impact is positive on the rest of the sub-samples. This may seem

to contradict the fact that firms do not increase their production with their own R&D

20

expenditures26; however, the lack of ability to turn investments into innovation does not mean

they cannot benefit indirectly from external knowledge. A possible explanation might be that

firms use the existing external knowledge in the sector to imitate or “copy” what other firms do,

thus acting as “free-riders”. By doing so, they benefit from the R&D expenditures of others and

increase their sales. Additionally, it might reflect the fact that firms observe the results of their

competitors’ investment (their successes or failures) and learn from them, using this external

pool of knowledge to be more efficient in their own production.

In contrast to Beneito’s (2001) findings, here it can be seen that firms operating in advanced

high-tech sectors (columns 3 and 5) benefit less from the R&D expenditures made by all other

firms in their sector than is the case of firms in less technologically advanced sectors (columns 2

and 4). There would appear to be, therefore, a “technology threshold” beyond which firms

benefit less from the R&D expenditures made by all other firms in the sector. This might reflect

the fact that in high-tech sectors (especially in KIS) there is a competition effect which

compensates for (or even dominates) the benefits stemming from external R&D, unlike the

situation that prevails in low-tech sectors.

In the breakdown by firm size there appears to be a positive coefficient only for large firms,

while small and medium firms reduce their sales if the rest of the firms in the same sector

increase their R&D expenditures. Thereby, the larger the firm, the greater is the benefit reaped

from the R&D expenditures made by all the other firms in the same sector. A possible

explanation for this might be that large firms have technological expertise and managerial skills

as well as better infrastructure and higher resources which allow them to maximize the benefit

from external innovations.

As for inter-industry externalities, our estimates present a high degree of heterogeneity. First,

in the sample as a whole (column 1) they affect firm’s sales negatively. Second, when the sample

is broken down according to technology level (columns 2 to 5), these externalities are not

significant for firms operating in low-tech manufacturing firms. This suggests that firm

performance is not influenced by the innovation carried out by the rest of the sectors that serve

as its suppliers. On the contrary, their effect is positive and significant in the case of high-tech

manufacturing firms. This result is consistent with the “absorption capacity” hypothesis

forwarded by Cohen and Levinthal (1989), which suggests that the degree to which a firm

benefits from external innovation is strongly dependent on its own innovation expenditure.

Thus, firms with greater technological capital are the ones that obtain the most benefits from

externalities. Advanced firms have better infrastructure and are probably more capable of

understanding and integrating external knowledge in their products and processes. In the case

of the service sector, once again knowledge-intensive service presents a negative coefficient.

This finding could be related to a price increase, given that suppliers may charge more after

investing in R&D and, so, the firm also raises its prices. Thus, in a competitive environment, as

in knowledge-intensive sectors, this might result in a reduction in sales. On the other hand, inter-

industry externalities show a positive and significant coefficient for non-knowledge-intensive

services, i.e. firms belonging to these sectors increase their performance thanks to the R&D

expenditures of firms operating in other sectors. This result could reflect the complementarity

of the technology effort between the suppliers sectors and non-knowledge-intensive services.

It should be remembered that non-knowledge-intensive services present the lowest R&D stock

26 We would like to thank an anonymous referee for pointing this out.

21

(see Table 2), which would explain why they benefit most from the investments made by all the

other firms (both in their own sector and in other sectors). Finally, in the breakdown by firm size,

inter-industry externalities are not relevant in general, although they present a negative

coefficient for small firms.

Finally, as can be seen from Table 4, control variables are, in general, significant. The trend

captures the effect of the economic crisis. Firm size has a positive impact, i.e., the larger the

firm, the more productive it is. Finally, the dummy variables for technology levels show that

high-tech manufacturing and non-knowledge-intensive service firms present higher sales than

low-tech manufacturing firms. By contrast, knowledge-intensive services present a lower

coefficient.

5.3. Further explorations

This section seeks to shed some light on the non-significant impact of R&D stock on firms’ sales

obtained in Table 4. In line with Crépon, Duguet and Mairesse (1998), there would appear to be

a process that leads from R&D investments to innovation outputs and from there to

productivity. Thus, here, as an initial attempt at adopting this new approach, innovation is

defined from an output perspective. Specifically, innovation is proxied with a dummy variable

that takes a value of 1 if the firm declares that it has obtained a product or process innovation

during the period [t-2, t]. It is expected that firm’s sales in time t increase if the firm has obtained

an innovation output in the preceding three years.

Table 5 OP estimates. Estimation results of Equation [4] using innovation output. 2004-2009. Dependent variable: ln(sales).

Total LTMI HTMI NKIS KIS Small Medium Large

(1) (2) (3) (4) (5) (6) (7) (8)

𝑘𝑖𝑠𝑡 0.0439*** 0.0434*** 0.0231*** 0.0163 0.0771*** 0.0331*** 0.0626*** 0.0375***

(0.0089) (0.0117) (0.0083) (0.0191) (0.0181) (0.0075) (0.0164) (0.0134)

𝑙𝑖𝑠𝑡 0.8298*** 0.9443*** 1.0264*** 0.8290*** 0.7483*** 1.0676*** 0.9015*** 0.7241***

(0.0173) (0.0319) (0.0282) (0.0449) (0.0306) (0.0285) (0.0403) (0.0254)

ℎ𝑖𝑠𝑡 0.0045*** 0.0074*** 0.0042*** 0.0092*** 0.0033*** 0.0021*** 0.0048*** 0.0078***

(0.0005) (0.0009) (0.0006) (0.0016) (0.0005) (0.0006) (0.0008) (0.0008)

𝑖𝑖𝑠𝑡 0.1716*** 0.0500 0.0853** 0.1562*** 0.2685*** 0.0961*** 0.1316*** 0.1913***

(0.0203) (0.0337) (0.0387) (0.0541) (0.0435) (0.0288) (0.0412) (0.0380)

𝑆𝑖𝑠𝑡−2𝑖𝑛𝑡𝑟𝑎 -0.0000 0.0004*** 0.0000* 0.0010*** -0.0002*** -0.0001** -0.0001** 0.0001***

(0.0000) (0.0000) (0.0000) (0.0001) (0.0000) (0.0000) (0.0000) (0.0000)

𝑆𝑖𝑠𝑡−2𝑖𝑛𝑡𝑒𝑟 -0.0002* -0.0001 0.0004** 0.0007*** -0.0011*** -0.0002** -0.0000 -0.0001

(0.0001) (0.0001) (0.0002) (0.0001) (0.0002) (0.0001) (0.0001) (0.0002)

Trend -0.0162** -0.0415*** -0.0871*** -0.1958*** 0.1085*** -0.0094 -0.0175 -0.0289*

(0.0073) (0.0089) (0.0172) (0.0166) (0.0150) (0.0104) (0.0126) (0.0168)

Medium 0.2166*** 0.1256*** 0.0554 0.2886*** 0.1885***

(0.0271) (0.0457) (0.0445) (0.0905) (0.0593)

Large 0.1751*** 0.0356 -0.0977 0.2635* 0.1714*

22

(0.0489) (0.0825) (0.0769) (0.1481) (0.0953)

HTI 0.1029*** 0.1655*** 0.1514*** -0.0640

(0.0253) (0.0359) (0.0407) (0.0683)

NKIS 0.1346*** 0.4292*** 0.2727*** -0.0596

(0.0351) (0.0626) (0.0689) (0.0644)

KIS -0.7127*** -0.5618*** -0.7877*** -0.7124***

(0.0360) (0.0483) (0.0691) (0.0632)

Firms 8,638 3,023 2,177 1,116 2,322 3,962 2,856 2,574

Obs 23,004 7,990 5,891 3,027 6,096 9,461 6,680 6,863

Note: Low and medium-low tech manufacturing firms (LTMI), medium-high and high tech manufacturing firms

(HTMI), non-knowledge-intensive services (NKIS), knowledge-intensive services (KIS). Externalities are lagged two

periods. Reference groups: LTMI and small firms. Bootstrapped errors in parenthesis.*** Significant at 1%, **

significant at 5%, * significant at 10%.

As can be seen in Table 5, when adopting this new definition, innovation is clearly positive and

significant in the sample as a whole and in all sub-samples. Although, as discussed above, the

aim of this article is not to undertake a structural analysis, the results of this section are highly

informative as they suggest that Spanish firms are able to generate a profit and increase their

sales from their innovation outcomes. As such what needs to be explored in greater depth is the

ability to turn R&D investment into innovations outputs. In line with the literature, advanced

firms (HTMI and KIS) present a greater impact than less advanced companies. In addition, the

larger the firm is, the higher the impact of innovation on firm sales. Finally, with regard to

externalities, the results in Table 5 are very similar to those obtained earlier.

6. Conclusions

This paper has studied the extent to which the level of technology of a firm and its size affect

the return that firms in Spain can obtain from their own investment in R&D activities and from

the R&D carried out by all other firms (both in the same sector and in other sectors). In

particular, the aim of this research can be summed up in three questions: (i) Does the impact of

innovation on firm performance differ according to a firm’s level of technology and/or firm size?

(ii) Are Spanish firms able to benefit from externalities? (iii) And if so, do these benefits vary

according to a firm’s technology level and/or firm size?

A Cobb-Douglas production function has been employed, including not only firm own R&D

investments but also intra-industry and inter-industry externalities. The empirical analysis is

applied to a panel data of Spanish firms from 2004 to 2009 using the Olley and Pakes estimator

to account for simultaneity and selection bias. By doing so, it is possible to obtain robust and

consistent estimates allowing for firm heterogeneity, simultaneity and selection biases.

As we have seen in the descriptive analysis, significant differences were found in this regard

according to the technology level of the sector in which a firm operates and according to firm

size. Specifically, human capital levels present notable differences according to the sub-samples

under consideration, being much greater in high-tech and small firms. Innovation effort is also

clearly greater in high-tech firms (in both industrial and in service sectors) and in medium-sized

firms. Therefore, in order to take these differences into account, we estimated each sub-sample

separately.

23

The following conclusions can be drawn. In the first place, when the whole sample is analysed,

we find that physical capital, labour, human capital and R&D stock present a positive coefficient.

Externalities are also significant even though they are negative. On the other hand, the dummy

variables included in the regressions to control for technology level and firm size suggest that

there are, in fact, differences according to these factors.

In the second place, once we distinguish between technology level of the sector in which the

firm operates and firm size, we see that the larger the firm, the more benefits it derives from

physical capital investment. As far as human capital is concerned, the results show a positive

impact. In particular, the larger the firm, the more benefits it obtains from hiring qualified

workers and the better its performance is. Regarding R&D investment, our results suggest that

R&D expenditures do not have a direct impact on firm sales. Although this result is unexpected,

it is in line with the Crépon, Duguet and Mairesse (1998) approach, which holds that there is a

sequential process leading from R&D to innovation outputs and from there to productivity.

Therefore, the result obtained here, although somewhat surprising, provides us with valuable

information in this regard. Specifically, when innovation is proxied by innovation output (as

opposed to innovation input, i.e., R&D), the results clearly present positive coefficients.

Therefore, with respect to the first research question, it can be concluded that the impact of

innovation output differs according to the level of technology and firm size, being notably

greater for those firms that operate in high-tech sectors (HTMI and KIS) and increasing with firm

size.

Interestingly, the “inability” of firms to benefit from their own R&D efforts does not prevent

them from benefiting from external knowledge. In particular, it has been seen that, in most

technology levels Spanish firms increase their sales when the rest of the firms in their sector

increase their R&D expenditures. Specifically, low-tech sectors manage to benefit to a greater

extent from the R&D expenditure carried out by all the other firms in the same sector. This result

could indicate that firms of this type seek to compensate for their smaller investment effort by

taking advantage of innovation originating in firms in the same sector. It might also reflect the

fact that high-tech sectors face higher competition, probably due to the higher degree of

technology, which compensates for or dominates, the benefits stemming from external R&D. All

in all, the results seems to point out the existence of a “technology threshold” beyond which

firms cease to benefit from the investments in R&D carried out by all the other firms in the same

sector. On the other hand, firm size reveals that only large firms increase their sales with external

R&D.

Inter-industry externalities have been shown to play an ambiguous role and there would appear

to be no specific pattern of behaviour associated with different levels of technology or firm size.

However, the results suggest the presence of a certain complementarity for the technological

efforts with supplier sectors in the case of firms belonging to high-tech manufacturing industries

and non-knowledge-intensive services.

In short, and in answer to our second research question, it has been shown that Spanish firms

are able to benefit from spillovers. Moreover, these benefits depend to some extent on the

technology level and firm size – which results in an affirmative answer to our last question. More

specifically, technology level and firm size appear to have an influence on intra-industry

externalities, i.e., the less advanced and the bigger the firm, the more benefits it derives from

intra-industry externalities, while their effect is much more ambiguous in the case of inter-

industry externalities.

24

Appendixes

Appendix A: Treatment of extreme values

The table below reports the number of firms with more than double the volume of sales by

technological level. These observations have been replaced by the double of sales.

Table A1. Outliers by technological level

More than 2* Sales LTI HTI NKIS KIS Total

Investment intensity 51 18 39 162 270

R&D expenditures 19 23 9 335 386

Table A2: Outliers by firm size

More than 2* Sales Small Medium Large Total

Investment intensity 139 56 75 270

R&D expenditures 275 88 23 386

Appendix B: Correspondence between branches of business activity

Table B1. Correspondence between PITEC and NACE-Rev. 1.1classification

Branches of business activity by PITEC NACE Rev 1.1

Low-tech manufacturing firms

Food products and beverages 15

Tobacco 16

Textile products 17

Clothing and furriers 18

Leather and leather products 19

Wood and wood products 20

Pulp, paper and paper products 21

Publishing and printing 22

Furniture 361

Games and toys 365

Other manufactures 36 (exc. 361, 365)

Recycling 37

Medium-low-tech manufacturing firms

Rubber and plastic products 25

Ceramic tiles and flags 263

Non-metallic mineral products (except tiles and flags) 26 (exc. 263)

Ferrous metallurgic products 271, 272, 273, 2751, 2752

Non-ferrous metallurgic products 274, 2753, 2754

Metal products (except machinery and equipment) 28

Building and repairing of ships and boats 351

Medium-high-tech manufacturing firms

Chemical products (except pharmaceuticals) 24 (exc. 244)

Machinery and equipment 29

Electrical machinery and apparatus 31

Motor vehicles, trailer and semi-trailers 34

Other transport equipment 35 (exc. 351, 353)

High-tech manufacturing firms

25

Manufacture of pharmaceutical products 244

Office machinery and computers 30

Electronic components 321

Radio, TV and communication equipment and apparatus 32 (exc. 321)

Medical, precision and optical instruments, watches and clocks 33

Aircraft and spacecraft 353

Non-knowledge-intensive services

Sales and repair of motor vehicles 50

Wholesale trade 51

Retail trade 52

Hotels and restaurants 55

Transport 60, 61, 62

Supporting and auxiliary transport activities, travel agencies 63

To be continued on the next page

Table B.1 – Continued from the previous page

Branches of business activity by PITEC NACE Rev 1.1

Knowledge-intensive services

Post 641

Telecommunications 642

Financial intermediation 65, 66, 67

Real estate activities 70

Renting of machinery and equipment 71

Computer activities 722

Other related computer activities 72 (exc.722)

Research and development 73

Architectural and engineering activities 742

Technical testing and analysis 743

Other business activities 74 (exc. 742, 743)

Education 80 (exc. 8030)

Motion picture, video and television programme production 921

Programming and broadcasting activities 922

Other human health and social activities 85, 90, 91, 92 (exc. 921,922), 93

Source: PITEC and Eurostat.

Appendix C: Distribution of firms according sub-samples

Table C1: Sample distribution

Small Medium Large Total

LTMI Observations 7,858 6,162 3,775 17,795

(% row) 44.16% 34.63% 21.21% 100%

(% column) 35.12% 42.78% 25.46% 34.48%

HTMI Observations 6,438 4,036 2,548 13,022

(% row) 49.44% 30.99% 19.57% 100%

(% column) 28.78% 28.02% 17.18% 25.23%

26

NKIS Observations 1,943 1,269 3,425 6,637

(% row) 29.28% 19.12% 51.60% 100%

(% column) 8.68% 8.81% 23.10% 12.86%

KIS Observations 6,133 2,936 5,081 14,150

(% row) 43.34% 20.75% 35.91% 100%

(% column) 27.41% 20.38% 34.26% 27.42%

Total Observations 22,372 14,403 14,829 51,604

(% row) 43.35% 27.91% 28.74% 100%

(% column) 100% 100% 100% 100%

Source: PITEC; own calculations.

27

Appendix D: Intermediate results

Table D1. Results from the partially linear model (Equation [13]) Total LTMI HTMI NKIS KIS Small Medium Large (1) (2) (3) (4) (5) (6) (7) (8)

k -0.0450 -0.0153 -0.0824 0.0139 -0.0675 0.0990* -0.0562 -0.2781*** (0.0277) (0.0409) (0.0552) (0.0840) (0.0629) (0.0544) (0.0700) (0.0683) i 0.0491 -0.0995 -0.0672 0.0940 0.1350 -0.0154 -0.2462** 0.1489** (0.0356) (0.0615) (0.0684) (0.1076) (0.0852) (0.0756) (0.0990) (0.0723) l 0.8187*** 0.9235*** 0.9861*** 0.8117*** 0.7437*** 1.0445*** 0.9094*** 0.7024*** (0.0162) (0.0313) (0.0304) (0.0466) (0.0306) (0.0270) (0.0404) (0.0255) h 0.0030*** 0.0060*** 0.0026*** 0.0085*** 0.0019*** 0.0011** 0.0034*** 0.0070*** (0.0005) (0.0010) (0.0008) (0.0017) (0.0006) (0.0005) (0.0009) (0.0009) Sintra -0.0000** 0.0004*** 0.0000* 0.0010*** -0.0003*** -0.0001*** -0.0001*** 0.0001*** (0.0000) (0.0001) (0.0000) (0.0001) (0.0000) (0.0000) (0.0000) (0.0000) Sinter -0.0002*** -0.0001 0.0004** 0.0007*** -0.0012*** -0.0003*** -0.0001 -0.0002 (0.0001) (0.0001) (0.0002) (0.0001) (0.0002) (0.0001) (0.0001) (0.0002) trend -0.0118* -0.0364*** -0.0853*** -0.1905*** 0.1215*** -0.0045 -0.0175 -0.0222 (0.0063) (0.0091) (0.0175) (0.0155) (0.0169) (0.0097) (0.0116) (0.0141)

HTI 0.0953*** -- -- -- -- 0.1595*** 0.1301*** -0.0879 (0.0254) (0.0373) (0.0428) (0.0598) NKIS 0.1478*** -- -- -- -- 0.4437*** 0.3305*** -0.0766 (0.0402) (0.0458) (0.0618) (0.0566) KIS -0.7088*** -- -- -- -- -0.5722*** -0.7679*** -0.7004*** (0.0300) (0.0479) (0.0677) (0.0560)

Medium 0.2168*** 0.1143** 0.0694* 0.3132*** 0.1913*** -- -- -- (0.0289) (0.0476) (0.0418) (0.1056) (0.0627) Large 0.2035*** 0.0383 -0.0667 0.2901* 0.2287** -- -- -- (0.0494) (0.0786) (0.0747) (0.1641) (0.1036)

inv -0.1516*** -0.1187*** 0.0395 -0.1375 -0.1997*** -0.0278 0.0228 -0.1403** (0.0359) (0.0422) (0.0662) (0.0849) (0.0689) (0.0780) (0.0780) (0.0634) inv *inv 0.0164*** 0.0180*** -0.0008 0.0120* 0.0135** 0.0023 -0.0001 0.0253*** (0.0035) (0.0044) (0.0054) (0.0067) (0.0059) (0.0082) (0.0092) (0.0061) k * inv 0.0058* 0.0019 -0.0037 0.0050 0.0162* 0.0016 -0.0075 -0.0054 (0.0034) (0.0046) (0.0066) (0.0110) (0.0087) (0.0073) (0.0064) (0.0078) i * inv -0.0003 -0.0043 -0.0020 0.0010 0.0035 -0.0008 -0.0013 -0.0051 (0.0022) (0.0030) (0.0048) (0.0048) (0.0049) (0.0041) (0.0052) (0.0060) k * k -0.0017 -0.0049 0.0088 -0.0068 -0.0060 -0.0170** 0.0044 0.0207*** (0.0033) (0.0052) (0.0059) (0.0095) (0.0077) (0.0073) (0.0093) (0.0079) k * i -0.0011 0.0091* 0.0046 0.0006 -0.0056 0.0005 0.0102 -0.0102 (0.0034) (0.0047) (0.0070) (0.0076) (0.0085) (0.0058) (0.0077) (0.0065) i * i -0.0037 0.0096 0.0058 -0.0128 -0.0113 0.0021 0.0336*** -0.0017 (0.0042) (0.0091) (0.0079) (0.0130) (0.0100) (0.0101) (0.0108) (0.0078)

inv *inv * inv 0.0002 -0.0003 -0.0000 0.0001 0.0011*** 0.0004 0.0004 -0.0001 (0.0002) (0.0003) (0.0004) (0.0006) (0.0004) (0.0004) (0.0004) (0.0004) k * inv * inv -0.0009*** -0.0002 0.0002 -0.0006 -0.0023*** -0.0005 0.0000 -0.0010* (0.0003) (0.0004) (0.0005) (0.0008) (0.0006) (0.0006) (0.0005) (0.0006) i * inv * inv -0.0000 -0.0001 0.0002 -0.0007*** -0.0000 0.0001 -0.0004* -0.0001 (0.0001) (0.0001) (0.0003) (0.0002) (0.0002) (0.0002) (0.0002) (0.0001) k * k * inv 0.0001 -0.0002 0.0000 -0.0000 0.0002 0.0001 0.0001 0.0004* (0.0001) (0.0002) (0.0002) (0.0003) (0.0003) (0.0001) (0.0002) (0.0002) k * i * inv 0.0003** 0.0003 0.0002 0.0002 0.0006* 0.0004 0.0005 0.0004 (0.0001) (0.0002) (0.0004) (0.0003) (0.0004) (0.0003) (0.0004) (0.0003) i * i * inv -0.0003** 0.0000 -0.0002 0.0004 -0.0010*** -0.0005*** -0.0001 -0.0001 (0.0001) (0.0002) (0.0002) (0.0003) (0.0003) (0.0002) (0.0002) (0.0004) k * k * k 0.0004*** 0.0005*** -0.0001 0.0005* 0.0008*** 0.0008*** 0.0001 -0.0001 (0.0001) (0.0002) (0.0002) (0.0003) (0.0003) (0.0003) (0.0003) (0.0002) k * k * i -0.0002 -0.0001 -0.0001 0.0001 -0.0005 0.0000 -0.0002 -0.0004* (0.0001) (0.0002) (0.0003) (0.0003) (0.0003) (0.0002) (0.0003) (0.0002) k * i * i 0.0001 -0.0008** -0.0004 -0.0005 0.0008* -0.0003 -0.0008* 0.0011*** (0.0002) (0.0004) (0.0004) (0.0007) (0.0005) (0.0004) (0.0004) (0.0004) i * i * i 0.0002 0.0001 0.0001 0.0008 0.0004 0.0003 -0.0007* -0.0004 (0.0002) (0.0004) (0.0004) (0.0005) (0.0004) (0.0005) (0.0004) (0.0003)

Constant 12.1286*** 11.6123*** 11.5123*** 12.4945*** 11.7280*** 11.2497*** 11.7429*** 13.3758*** (0.0773) (0.1462) (0.1773) (0.1916) (0.1407) (0.1096) (0.2292) (0.2051) Observations 41,619 14,415 10,557 5,376 11,271 17,753 11,802 12,604

Note: Low and medium-low tech manufacturing firms (LTMI), medium-high and high tech manufacturing firms (HTMI), non-knowledge-intensive services (NKIS), knowledge-intensive services (KIS). Externalities are lagged two periods. Reference groups: LTMI and small firms. Bootstrapped errors in parenthesis.*** Significant at 1%, **

28

significant at 5%, * significant at 10%. “inv” stands for investment in both physical capital and R&D (in logs) and it is our proxy variable.

Table D2. Results from Probit model (Equation [14]) Total LTMI HTMI NKIS KIS Small Medium Large (1) (2) (3) (4) (5) (6) (7) (8)

inv t-1 0.0237 -0.0205 0.0100 -0.1682 0.0994 0.0246 0.0760 0.1468 (0.0432) (0.0899) (0.1306) (0.1656) (0.0783) (0.0805) (0.1572) (0.1285) k t-1 0.0596 0.0156 0.1476 0.1809 0.0158 0.1528** 0.0256 -0.0497 (0.0378) (0.0751) (0.0933) (0.1239) (0.0602) (0.0640) (0.1279) (0.0970) i t-1 -0.0245 0.0682 -0.0649 -0.4397* -0.1275 -0.0572 -0.1178 0.0693 (0.0474) (0.1022) (0.1379) (0.2561) (0.0909) (0.0892) (0.1678) (0.1611)

(inv * inv) t-1 -0.0150** -0.0103 -0.0047 -0.0077 -0.0201** -0.0165 -0.0253 -0.0381*** (0.0063) (0.0119) (0.0160) (0.0191) (0.0098) (0.0128) (0.0196) (0.0147) (k * inv) t-1 0.0067 0.0079 0.0019 0.0232 0.0021 0.0062 0.0054 0.0164 (0.0049) (0.0073) (0.0119) (0.0277) (0.0083) (0.0065) (0.0154) (0.0172) (i * inv) t-1 0.0057 0.0035 0.0021 0.0219 0.0057 0.0066 0.0075 0.0192 (0.0035) (0.0077) (0.0117) (0.0182) (0.0064) (0.0051) (0.0107) (0.0211) (k * k) t-1 -0.0079* -0.0008 -0.0203* -0.0213 -0.0039 -0.0234*** -0.0029 0.0044 (0.0047) (0.0092) (0.0112) (0.0165) (0.0078) (0.0083) (0.0157) (0.0123) (k * i) t-1 -0.0055 -0.0119 0.0014 -0.0173 -0.0013 -0.0064 -0.0006 -0.0088 (0.0044) (0.0076) (0.0130) (0.0257) (0.0081) (0.0061) (0.0146) (0.0167) (i * i) t-1 0.0037 -0.0006 0.0029 0.0689* 0.0157 0.0087 0.0169 -0.0132 (0.0072) (0.0160) (0.0178) (0.0398) (0.0129) (0.0146) (0.0203) (0.0150)

(inv * inv * inv) t-1 0.0011*** 0.0014** 0.0008 -0.0005 0.0011* 0.0013** 0.0019** 0.0015* (0.0003) (0.0006) (0.0008) (0.0010) (0.0006) (0.0006) (0.0010) (0.0009) (k * inv * inv) t-1 -0.0009*** -0.0016** -0.0003 0.0012 -0.0009 -0.0007 -0.0016* -0.0004 (0.0004) (0.0007) (0.0011) (0.0016) (0.0008) (0.0006) (0.0009) (0.0014) (i *inv *inv) t-1 -0.0002 -0.0004 -0.0008 0.0005 0.0001 -0.0006** -0.0005 -0.0001 (0.0002) (0.0003) (0.0007) (0.0006) (0.0004) (0.0003) (0.0004) (0.0005) (k* k *inv) t-1 0.0002 0.0004 -0.0003 -0.0011 0.0004 -0.0001 0.0006 -0.0003 (0.0002) (0.0003) (0.0005) (0.0011) (0.0003) (0.0002) (0.0005) (0.0007) (k * i * inv) t-1 0.0001 0.0003 0.0006 -0.0018 0.0003 0.0002 0.0003 -0.0003 (0.0003) (0.0005) (0.0009) (0.0014) (0.0005) (0.0005) (0.0007) (0.0009) (i * i* inv) t-1 -0.0004** -0.0002 -0.0001 -0.0000 -0.0007* -0.0001 -0.0006 -0.0008 (0.0002) (0.0004) (0.0006) (0.0011) (0.0004) (0.0003) (0.0005) (0.0011) (k * k * k) t-1 0.0002 -0.0000 0.0006 0.0005 0.0002 0.0009*** 0.0001 -0.0001 (0.0002) (0.0003) (0.0004) (0.0006) (0.0003) (0.0003) (0.0005) (0.0004) (k * k* i) t-1 -0.0001 -0.0003 0.0002 0.0016 -0.0004 -0.0001 -0.0004 0.0001 (0.0002) (0.0003) (0.0006) (0.0011) (0.0003) (0.0003) (0.0005) (0.0006) (k *i * i) t-1 0.0005* 0.0013*** -0.0004 -0.0005 0.0004 0.0005 0.0006 0.0004 (0.0003) (0.0005) (0.0008) (0.0012) (0.0005) (0.0004) (0.0008) (0.0008) (i * i * i) t-1 -0.0001 -0.0005 0.0003 -0.0028 -0.0004 -0.0004 -0.0006 0.0006 (0.0003) (0.0007) (0.0008) (0.0021) (0.0006) (0.0007) (0.0008) (0.0007)

tend t-1 -0.0634*** -0.0330** -0.0728*** -0.0386 -0.0981*** -0.0626*** -0.0569*** -0.0830*** (0.0079) (0.0142) (0.0164) (0.0242) (0.0146) (0.0111) (0.0189) (0.0148) hti t-1 -0.0272 -- -- -- -- -0.0631 0.0039 0.1154 (0.0309) (0.0473) (0.0638) (0.0889) nkis t-1 -0.1236*** -- -- -- -- -0.1612** -0.1777 0.0309 (0.0451) (0.0630) (0.1138) (0.0832) kis t-1 0.0202 -- -- -- -- 0.0410 0.0838 0.0475 (0.0316) (0.0423) (0.0815) (0.0791) medium t-1 -0.1505*** -0.1279*** -0.0974* -0.1758 -0.1802*** -- -- -- (0.0252) (0.0471) (0.0562) (0.1089) (0.0573) large t-1 -0.2622*** -0.2315*** -0.1079 -0.2249** -0.3580*** -- -- -- (0.0389) (0.0858) (0.1013) (0.1122) (0.0680)

Constant -1.1913*** -1.2589*** -1.2267*** -1.3436*** -1.0823*** -1.0501*** -1.5051*** -1.7120*** (0.0461) (0.0730) (0.1174) (0.1308) (0.0945) (0.0580) (0.1171) (0.1219) Observations 32,119 11,150 8,182 4,179 8,608 13,462 9,220 9,437

Note: Low and medium-low tech manufacturing firms (LTMI), medium-high and high tech manufacturing firms (HTMI), non-knowledge-intensive services (NKIS), knowledge-intensive services (KIS). Externalities are lagged two periods. Reference groups: LTMI and small firms. Bootstrapped errors in parenthesis.*** Significant at 1%, ** significant at 5%, * significant at 10%. “inv” stands for investment in both physical capital and R&D (in logs) and it is our proxy variable.

29

Acknowledgements

This research has received funding from the European Union's Seventh Framework Programme

FP7-SSH-2010-2.2-1 (2011-2014), under grant agreement n° 266834. The authors are grateful

for the funding obtained from the Ministry of Education and Science for the project entitled

“Determinants for the spread of innovation and their effects on productivity”, ECO2009-

12678/ECON, 2010-2012. Esther Goya is grateful for the support received from the CUR of the

DIUE of the Generalitat de Catalunya and from the European Social Fund. Esther Vayá is grateful

for the funding obtained from the Ministry of Education and Science for the project entitled

“Regional economic growth and inequality in Spain”, ECO2010-16006/ ECON, 2011-2013. Jordi

Suriñach is grateful for the funding obtained from the Ministry of Education and Science for the

project ECO2013-41022-R. The authors are grateful for helpful comments received at the AQR

Lunch Seminar celebrated on the 9th of October, 2012 at the University of Barcelona.

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